Leveraging experience for robust, adaptive nonlinear MPC on computationally constrained systems with time-varying state uncertainty
| Autor: | Lauren Lieu, Vishnu R. Desaraju, Nathan Michael, Cormac O'Meadhra, Alexander Spitzer |
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| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Computer science Applied Mathematics Mechanical Engineering 02 engineering and technology Belief propagation Nonlinear system Model predictive control 020901 industrial engineering & automation Artificial Intelligence Control theory Modeling and Simulation Nonlinear model 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing State (computer science) Electrical and Electronic Engineering Software |
| Zdroj: | The International Journal of Robotics Research. 37:1690-1712 |
| ISSN: | 1741-3176 0278-3649 |
| DOI: | 10.1177/0278364918793717 |
| Popis: | This paper presents a robust-adaptive nonlinear model predictive control (MPC) technique that leverages past experiences to achieve tractability on computationally constrained systems. We propose a robust extension of the Experience-driven Predictive Control (EPC) algorithm via a Gaussian belief propagation strategy that computes an uncertainty set, bounding the evolution of the system state in the presence of time-varying state uncertainty. This uncertainty set is used to tighten the constraints in the predictive control formulation via a chance-constrained approach, thereby providing a probabilistic guarantee of constraint satisfaction. The parameterized form of the controllers produced by EPC coupled with online uncertainty estimates ensures that this robust constraint satisfaction property persists, even as the system switches controllers and experiences variations in the uncertainty model. We validate the online performance and robust constraint satisfaction of the proposed Robust EPC algorithm through a series of trials with a simulated ground robot and three experimental platforms: (1) a small quadrotor aerial robot executing aggressive maneuvers in wind with degraded state estimates, (2) a skid-steer ground robot equipped with a laser-based localization system, and (3) a hexarotor aerial robot equipped with a vision-based localization system. |
| Databáze: | OpenAIRE |
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